한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제23권4호
  • /
  • Pages.147-153
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  • 2018
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Development of ResNet-based WBC Classification Algorithm Using Super-pixel Image Segmentation

  • Lee, Kyu-Man (Department of Conversing Technology, Graduate School of Venture, Hoseo University) ;
  • Kang, Soon-Ah (Department of Conversing Technology, Graduate School of Venture and Institute of Health Industry, Hoseo University)
  • 투고 : 2018.03.27
  • 심사 : 2018.04.11
  • 발행 : 2018.04.30
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초록

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

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